Search

Background: Interstitial lung abnormalities (ILAs) on CT may affect the clinical outcomes in patients with chronic obstructive pulmonary disease (COPD), but their quantification remains unestablished. This study examined whether artificial intelligence (AI)-based segmentation could be applied to identify ILAs using two COPD cohorts., Methods: ILAs were diagnosed visually based on the Fleischner Society definition. Using an AI-based method, ground-glass opacities, reticulations, and honeycombing were segmented, and their volumes were summed to obtain the percentage ratio of interstitial lung disease-associated volume to total lung volume (ILDvol%). The optimal ILDvol% threshold for ILA detection was determined in cross-sectional data of the discovery and validation cohorts. The 5-year longitudinal changes in ILDvol% were calculated in discovery cohort patients who underwent baseline and follow-up CT scans., Results: ILAs were found in 32 (14%) and 15 (10%) patients with COPD in the discovery (n = 234) and validation (n = 153) cohorts, respectively. ILDvol% was higher in patients with ILAs than in those without ILA in both cohorts. The optimal ILDvol% threshold in the discovery cohort was 1.203%, and good sensitivity and specificity (93.3% and 76.3%) were confirmed in the validation cohort. 124 patients took follow-up CT scan during 5 ± 1 years. 8 out of 124 patients (7%) developed ILAs. In a multivariable model, an increase in ILDvol% was associated with ILA development after adjusting for age, sex, BMI, and smoking exposure., Conclusion: AI-based CT quantification of ILDvol% may be a reproducible method for identifying and monitoring ILAs in patients with COPD., (© 2024. The Author(s).)

Background: Facioscapulohumeral muscular dystrophy (FSHD) is an autosomal dominant muscular disorder characterized by asymmetric muscle wasting and weakness. FSHD can be subdivided into two types: FSHD1, caused by contraction of the D4Z4 repeat on chromosome 4q35, and FSHD2, caused by mild contraction of the D4Z4 repeat plus aberrant hypomethylation mediated by genetic variants in SMCHD1, DNMT3B, or LRIF1. Genetic diagnosis of FSHD is challenging because of the complex procedures required., Methods: We applied Nanopore CRISPR/Cas9-targeted resequencing for the diagnosis of FSHD by simultaneous detection of D4Z4 repeat length and methylation status at nucleotide level in genetically-confirmed and suspected patients., Results: We found significant hypomethylation of contracted 4q-D4Z4 repeats in FSHD1, and both 4q- and 10q-D4Z4 repeats in FSHD2. We also found that the hypomethylation in the contracted D4Z4 in FSHD1 is moderately correlated with patient phenotypes., Conclusions: Our method contributes to the development for the diagnosis of FSHD using Nanopore long-read sequencing. This finding might give insight into the mechanisms by which repeat contraction causes disease pathogenesis., (© 2022. The Author(s).)

Background: Quantitative computed tomography (CT) analysis has been proposed as a means of objectively assessing fibrotic interstitial pneumonia (IP) including idiopathic pulmonary fibrosis (IPF). We investigated whether percentages of high-attenuation areas (HAA%) and cystic areas (CA%) quantified from CT images were useful as indices of fibrotic IP., Methods: CT images of 74 patients with fibrotic idiopathic interstitial pneumonias (IPF, 36; non-specific interstitial pneumonia, 9; unclassifiable idiopathic interstitial pneumonia, 29) were analyzed via in-house computer software, which automatically calculated HAA%, CA%, mean lung density (MLD), standard deviation of lung density (SD-LD), kurtosis, and skewness from CT attenuation histograms. These indices were compared in each instance with physiologic measures, visual fibrosis score, clinical diagnosis, radiologic CT pattern, and prognosis., Results: HAA% correlated significantly with physiologic measures and visual fibrosis score to a moderate extent (%forced vital capacity, rs = -0.59; % carbon monoxide diffusion capacity, rs = -0.43; fibrosis score, rs = 0.23). Densitometric parameters (MLD, SD-LD, kurtosis, and skewness) correlated significantly with physiologic measures and fibrosis score (|rs| = 0.28-0.59). CA% showed no association with pulmonary functions but differed significantly between IPF and other interstitial pneumonias (IPs) (1.50 ± 2.41% vs. 0.41 ± 0.80%; P < 0.01) and between the definite usual interstitial pneumonia (UIP) pattern and other patterns (1.48 ± 2.38% vs. 0.55 ± 1.19%; P < 0.01). On univariate analysis, HAA%, MLD, SD-LD, kurtosis, skewness, fibrosis score, and definite UIP pattern all correlated with survival, with kurtosis alone identified as a significant predictor of mortality on multivariate analysis (hazard ratio = 0.67; 95% CI, 0.44-0.96; P = 0.03)., Conclusion: CA% and HAA% are novel quantitative CT indices with differing properties in fibrotic IP evaluations. HAA% largely reflects physiologic impairments, whereas CA% corresponds with diagnosis and HRCT pattern. Of the CT indices examined, kurtosis constituted the strongest predictor of mortality.